1. Technical Field
The present invention relates to parallel processing and, in particular, to systems and methods for massively parallel, smart-memory-based processing in accelerators for data analytics.
2. Description of the Related Art
Applications that examine raw, unstructured data in order to draw conclusions and make decisions are becoming ubiquitous. Banks and credit cards companies, for instance, analyze withdrawal and spending patterns to prevent fraud or identity theft. Online retailers study website traffic patterns in order to predict customer interest in products and services based upon prior purchases and viewing trends. Semantic querying of text and images, which has wide-ranging, mass market uses such as advertisement placement and content-based image retrieval, is another fast growing application domain.
As the volume of data increases, the performance constraints on these applications become more stringent. As an example, for semantic text search, a server using a learning algorithm such as Supervised Semantic Indexing must search millions of documents at a few milliseconds per query. Another example is face and object recognition in high resolution video that is often done with Convolutional Neural Networks (CNNs). A server performing this task must search VGA (640×480) or higher resolution images at rates of 24 or more frames per second. Often, economic considerations dictate that multiple video streams be processed simultaneously on one server.